Blind detection

ABSTRACT

The present invention relates to a blind detection of a received signal having at least one property, which initially is unknown to the receiver, however, limited to a finite set of alternatives ({p 1 , p 2 , . . . , p m }). According to the invention, the unknown property is automatically identified as follows. A respective quality measure (q 1 , q 2 , . . . , q m ) is derived from the incoming signal (r(n)), which each represents a particular property in the finite set of alternatives ({p 1 , p 2 , . . . , p m }). The quality measures (q 1 , q 2 , . . . , q m ) are obtained according to a procedure that involves a computationally efficient rejection of any unwanted signal components in the incoming signal (r(n)). Then, the quality measures (q 1 , q 2 , . . . , q m ) are compared with each other in order to find a quality measure, which best fulfills a blind selection criterion. The quality measure (q i ) thus obtained corresponds to the initially unknown property (p i ). For instance, based on this information, the incoming signal (r(n)) may then be further processed in a processing unit ( 122 ), which operates according to a processing principle (C di ) that is adapted to this property (p i ).

THE BACKGROUND OF THE INVENTION AND PRIOR ART

The present invention relates generally to blind detection of receivedsignals having at least one property, which initially is unknown to thereceiver. More particularly the invention relates to a method ofidentifying a property from a finite set of alternatives of an incomingsignal according to the preamble of claim 1 and a blind signal detectoraccording to the preamble of claim 21. The invention also relates to acomputer program according to claim 19 and a computer readable mediumaccording to claim 20.

Many communication standards provide examples of situations where areceiver must be able to receive a certain signal whose format, at leastto some extent, is unknown. The receiver thus needs to perform a blinddetection of the signal, i.e. no signaling takes place between thetransmitter and the receiver before transmission according to theunknown format is initiated.

The GSM/EDGE-standard (GSM=Global System for Mobile communication;EDGE=Enhanced Data rates for Global Evolution) utilizes two differentmodulation schemes, namely GMSK (Gaussian Minimum Shift Keying) and 8PSK(Phase Shift Keying with eight different phase states). At start oftransmission the transmitter may use any of these modulation schemes.Furthermore, during transmission, the modulation schemes can be changed,without notice, between every radio block (i.e. between every set offour consecutive bursts). This transmitter behavior, of course, requiresa blind-detection capability of the receiver, at least with respect tosaid modulation schemes.

A theoretically conceivable solution would be to detect any receivedburst in parallel, both by means of a GMSK-equalizer and by means of an8PSK-equalizer. This would result in two estimated sequences of bitsthat correspond to the received signal. A checksum/parity test couldthen be used to determine which modulation scheme that was actuallyapplied when transmitting the sequence. The sequence detected under theincorrect modulation format would namely not pass such test. Naturally,the payload information contained in the received burst can also bederived through this solution simply by studying the sequencecorresponding to the correct modulation format. However, the solution isfar too computationally complex to be implemented in real timeapplications and is therefore not interesting from a technicalpoint-of-view. There is yet no alternative solution either, which issatisfying in this respect.

The standard document ETSI Tdoc SMG2 EDGE 2E99-279, ETSI SMG2 WorkingSession on EDGE, Montigny Le Bretonneux, France, 24-27 Aug., 1999presents a method for automatic detection of another unknown property ofa received signal, namely a training sequence, and how to select anappropriate detection principle for the received signal. The documentproposes that one out of three possible training sequences be identifiedaccording to the following procedure. First, a signal in the form of aradio burst is received. This signal is tested against a respectivehypothesis for each of the three possible training sequences. Thetraining sequence that corresponds to the hypothesis that results in thehighest estimated signal power of the received burst is then selected asthe training sequence having been used for the burst in question. Underideal conditions, this procedure generally generates selection decisionsof a sufficient accuracy. However, an actual radio environment isusually far from ideal. The received signal is hence more or lessdistorted by additive noise and/or interference signals. Theinterference signals typically originate from other radio stations,either transmitting at the same frequency/channel (so-called co-channelinterference) or transmitting at an adjacent frequency/channel(so-called adjacent channel interference).

The demodulation schemes of today's radio communication systems normallyinclude interference rejection algorithms in order to mitigate theeffects of any undesired signal components in the received signal, suchas noise and interference signals. In case a receiver in a system ofthis kind is required to make decisions pertaining to an unknownproperty of a received signal, and if these decisions per se do notinvolve interference rejection, there is a risk that the interferencerejection algorithms with respect to the detected signal become useless,namely if, due to interference, an incorrect blind detection decision istaken. Hence, if a corresponding interference rejection is not alsoincluded in the blind detection procedure, this procedure is prone to bethe limiting factor for the receiver performance, and consequently theentire system's performance. Presently, there exists no blind detectionprocedure, which involves interference rejection. Moreover, a directinclusion of any of the known interference rejection algorithms into theknown blind detection procedures would, again, impose a computationdemand on the receiver, which is too high to be performed in real time.

SUMMARY OF THE INVENTION

The object of the present invention is therefore to provide a blinddetection solution, which alleviates the problems above and thus bothproduces reliable blind detection decisions and is possible to carry outin real time.

According to one aspect of the invention the object is achieved by amethod of identifying, from a finite set of alternatives (hypotheses), aproperty of an incoming signal as initially described, which ischaracterized by deriving quality measures in consideration ofinterference rejection with respect to unwanted signal components in theincoming signal.

According to a preferred embodiment of the proposed method the incomingsignal is received in one reception branch and the interferencerejection is accomplished by means of calculating the quality measuresin consideration of a temporal whitening of the incoming signal.

According to another preferred embodiment of the proposed methodseparate versions of the incoming signal are received via at least twodifferent reception branches and the interference rejection isaccomplished by means of calculating the quality measures inconsideration of a spatio decorrelation of the separate versions of theincoming signal.

According to yet another preferred embodiment of the proposed methodseparate versions of the incoming signal are received via at least twodifferent reception branches and the interference rejection isaccomplished by means of calculating the quality measures in a combinedconsideration of a spatio decorrelation of the separate versions of theincoming signal and a temporal whitening of the incoming signal. Thus, aso-called spatio-temporal interference rejection is performed.

According to a further aspect of the invention the object is achieved bya computer program directly loadable into the internal memory of acomputer, comprising software for performing the above proposed methodwhen said program is run on a computer.

According to another aspect of the invention the object is achieved by acomputer readable medium, having a program recorded thereon, where theprogram is to make a computer perform the proposed method.

According to still another aspect of the invention the object isachieved by a blind signal detector as initially described, which ischaracterized in that each quality measure generator includes aninterference suppressor, which suppresses the effects of unwanted signalcomponents in the incoming signal.

The invention might place a computation demand on the receiver, which ismoderately higher than according to some of the previously knownsolutions for blind detection without interference rejection. In return,the invention offers a solution that is clearly superior to any of theknown blind detection methods in non-ideal signal environments.Naturally, this provides a competitive edge in most communicationsystems. Furthermore, the invention may be applied in a wide variety ofcommunication systems, irrespective of which type of signal format andtransmission medium that is employed.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is now to be explained more closely by means ofpreferred embodiments, which are disclosed as examples, and withreference to the attached drawings.

FIG. 1 shows a block diagram over a blind signal detector according tothe invention,

FIG. 2 shows a block diagram over a recursive unit, which is included ina quality measure comparator according to one embodiment of theinvention, and

FIG. 3 illustrates, by means of a flow diagram, a general methodaccording to the invention.

DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION

The principle for blind detection according to the invention isapplicable in communication systems using many transmission media otherthan radio. However, free space environments, where the signalstypically are transmitted by means of radio waves, generally impose morecomplex signal conditions than the alternative environments, where thesignals are more constrained by, for instance, being conducted viaelectrical cables or optical fibers. Therefore, a system model includinga time dispersive radio channel is introduced here in order to present ageneral framework for the invention.

A vector of M received signals, which are symbol space sampled, can bemodeled as:

$\begin{matrix}{{r(n)} = \begin{bmatrix}\begin{matrix}{r_{1}(n)} \\{r_{2}(n)}\end{matrix} \\\; \\{r_{M}(n)}\end{bmatrix}} \\{= {{\sum\limits_{m = 0}^{L}{\begin{bmatrix}\begin{matrix}{h_{1}(m)} \\{h_{2}(m)}\end{matrix} \\\; \\{h_{M}(m)}\end{bmatrix}{s\left( {n - m} \right)}}} + \begin{bmatrix}\begin{matrix}{v_{1}(n)} \\{v_{2}(n)}\end{matrix} \\\; \\{v_{M}(n)}\end{bmatrix}}} \\{= {{\sum\limits_{m = 0}^{L}{{h(m)}{s\left( {n - m} \right)}}} + {v(n)}}}\end{matrix}$where h(m) is a vector with a set of coefficients for a tap m in achannel having L+1 taps, s(n) represents the symbols that have beensent, and v(n) denotes additive noise, i.e. an unwanted signalcomponent. The above model is defined for a multi-branch system.Nevertheless, it is applicable also to a single-branch system simply bysetting M=1.

The additive noise v(n) can in turn be modeled by means of an autoregressive model:

${v(n)} = {{\sum\limits_{k = 1}^{K}{{A(k)}{v\left( {n - k} \right)}}} + {e(n)}}$where A_(k) is a matrix of auto regressive (AR) coefficients and e(n) isa noise vector, which is assumed to be temporally white and Gaussianwith a zero mean and covariance matrix Q:E{e(n)e(n)^(H) }=Qwhere ^(H) denotes a transpose and complex conjugate (Hermite).

The covariance matrix of the noise Q has a size of M×M elements and can,in turn, be expressed as:Q=C _(rr) −C _(rs) C _(ss) ⁻¹ C _(rs) ^(H)where

$C_{rr} = {\sum\limits_{n = L}^{N_{tr}}{{r\left( {n_{0} + n} \right)}{r^{H}\left( {n_{0} + n} \right)}}}$$C_{rs} = {\sum\limits_{n = L}^{N_{tr}}{{r\left( {n_{0} + n} \right)}{S^{H}\left( {n_{0} + n} \right)}}}$$C_{ss} = {\sum\limits_{n = L}^{N_{tr}}{{S(n)}{S^{H}(n)}}}$and S(n)=[s(n) s(n−1) . . . s(n−L)]^(T)

In the above expressions, n₀ denotes a synchronization position and s(n)represents a sequence of N_(tr)+1 training symbols. It should be notedthat the matrices above C_(rr) and C_(ss) are referred to as covariancematrices, even though they are not normalized with respect to the numberof samples. The covariance matrix of the desired signal C is expressedas:C=C _(rs) C _(ss) ⁻¹ C _(rs) ^(H)i.e. the last term in the expression for the covariance matrix of thenoise Q.

In the single reception branch case (i.e. M=1), the synchronizationposition n₀ is selected such that the residual noise variance σ²=Q isminimal. When instead, separate versions of the incoming signal areavailable via multiple reception branches (i.e. M>1), either tracesynchronization min{Trace{Q}} or determinant based synchronizationmin{|Q|} can be used for localizing the synchronization position n₀.

A least square channel estimate follows from the expression:[ĥ(0)ĥ(1) . . . ĥ(L)]=C _(rs) C _(ss) ⁻¹and the residual noise can be calculated as:

${e(n)} = {{{r\left( {n_{0} + n} \right)} - {\sum\limits_{m = 0}^{L}{{\hat{h}(m)}{s\left( {n - m} \right)}\mspace{76mu} L}}} \leq n \leq {N_{tr}.}}$

A temporal whitening of the noise in the incoming signals can beaccomplished according to a FIR (Finite Impulse Response) filtering:

${r_{w}(n)} = {\sum\limits_{k = 0}^{K}{{W(k)}{r\left( {n - k} \right)}}}$where the W(k) matrices represent the FIR coefficients, which are givenby:

${W(k)} = \left\{ \begin{matrix}{{- {\hat{A}(k)}},} & {{{for}\mspace{14mu} k} > 0} \\{I,} & {{{for}\mspace{14mu} k} = 0}\end{matrix} \right.$in which I is the identity matrix and the Â_(k) elements are estimatedwith, for instance, an indirect GLS (Generalized Least Square)multi-branch algorithm or a single-branch LDA (Levinson DurbinAlgorithm).

A whitening filter of FIR type can be constructed by estimating an autoregressive model of the noise, see the matrices W(k) above. Thiswhitening filter reduces the impact from any interference in theequalizer, which in turn improves the equalizer's performancesignificantly. Especially the adjacent channel interference can therebybe reduced. For instance, the estimation of the auto regressive modelcan be performed by an LDA.

A spatio noise decorrelation can be preformed according to theexpression:r _(wD)(n)=Dr _(w)(n)where D is a matrix with the following property: {circumflex over(Q)}⁻¹=D^(H)Dwith {circumflex over (Q)} being an estimate of the noise covariancematrix Q. The matrix D can be calculated by using a Choleskyfactorization scheme. It should be pointed out that the whitening andthe spatio noise decorrelation can be performed in the same step bymultiplying the matrix D with the matrices W(k) before carrying out theabove FIR filtering. Further details as how to accomplish a combinedspatio decorrelation and temporal interference rejection can be found inthe article “MLSE and Spatio-Temporal Interference Rejection Combiningwith Antenna Arrays”, Proceedings European Signal Processing Conference,pp 1341-1344, September 1998 by D. Asztély et al.

As mentioned earlier, blind detection is necessary in communicationsystems in which the transmitter may alter between two or moremodulation formats without prior announcement. The GSM/EDGE-standardconstitutes one such example, where either a GMSK or a 8PSK modulationscheme is used. A so-called derotation r_(GMSK)(n) of a received signalr(n), after which a quality measure that takes interference suppressioninto account may be calculated, can for the GMSK hypothesis be expressedas:r _(GMSK)(n)=e ^(−j) ² ^(π) ^(n) r(n).

A corresponding derotation r_(8PSK)(n) for the 8PSK hypothesis can beexpressed as:r _(8PSK)(n)=e ^(−j) ⁸ ^(3π) ^(n) r(n).

After derotation, the quality measures q_(GMSK)(n) and q_(8PSK)(n) canbe derived according to the same principle for both the GMSK hypothesisand the 8PSK hypothesis.

The incoming signal may either be a scalar (i.e. received in a singlebranch) or be multi-dimensional (i.e. received via two or morebranches).

An identifying procedure for finding a most probable modulation schemeinvolves comparing the quality measures q_(GMSK)(n) and q_(8PSK)(n) witheach other in order to determine which quality measure that bestfulfills a blind selection criterion. For example, if the qualitymeasures q_(GMSK)(n); q_(8PSK)(n) represent a respective signal-to-noiseratio (SNR) after whitening, the test would simply be to find thelargest quality measure value (i.e. best SNR). A correspondingmodulation scheme would then be regarded as the most probable for theincoming signal. Based on this information, a processing unit (e.g. anequalizer) that operates according to a matching demodulation scheme maythen be selected for further processing the incoming signal.

Either the same equalizer could be used in both the GMSK- and the8PSK-case, however with different parameter settings, or differentequalizer structures could be used for the different demodulationschemes, i.e. one equalizer designed for GMSK and another designed for8PSK.

Another example of a situation in which blind detection is necessary, iswhen different training sequences are employed by the transmitterwithout prior indication to the receiver. In certain radio communicationsystems the training sequence is namely not only used for estimatingradio channel properties in the receiver, the type of training sequenceper se also carries information. For example, a GSM/EDGE system, usesthe access burst to transport data in the random access channel (RACH)and the packet random access channel (PRACH). Three different trainingsequences, TS0, TS1 and TS2, are used by a mobile station to indicateits uplink capability. A base station receiving an uplink signal fromthe mobile station thus cannot know in advance which of the threepossible training sequences that will be sent. Therefore, the receiverin the base station must perform a blind detection in respect of thetraining sequence. The quality measures to be compared are here denotedq_(TS0), q_(TS1) and q_(TS2), one for each training sequence TS0, TS1and TS2 respectively. Depending on the quality measure q_(TS0), q_(TS1)or q_(TS2) for the incoming signal that best satisfies a blind selectioncriterion, a decision is subsequently made whether the training sequencewas TS0, TS1 or TS2.

It should be noted that a particular quality measure in turn may beconstituted by any linear or non-linear combination of sub-values.According to the invention, the quality measures nevertheless alwaystake interference suppression into account. Furthermore, the proposedmethod is applicable to any symbol sequence, i.e. not exclusively to adistinction between different training sequences.

FIG. 1 shows a block diagram over a general blind signal detectoraccording to the invention, which may be used for blind detection of theabove mentioned modulation formats, the training sequences as well asany other unknown property of an incoming signal, provided there is afinite number of alternatives for the unknown property. Particularly,the modulation formats may include any M-ary phase shift keying schemewith M≠8, such as 2PSK, 4PSK or 16PSK (i.e. for M equal 2, 4 and 16respectively).

An incoming signal r(n) is fed in parallel to at least two differentquality measure generators 101, 102 and 103. The FIG. 1 shows threequality measure generators, however any number larger than one isequally well conceivable. In any case, each quality measure generator101-103 is adapted to produce a quality measure q₁, q₂, . . . , q_(m) inrespect of one out of the possible alternatives for the unknown propertyp₁, p₂, . . . , p_(m). Nevertheless, in practice, two or more qualitymeasure generators may be represented by the same physical unit, theoperation of the generator then being determined by different settingsof at least one variable parameter. Consequently, if time allows, allthe quality measures q₁-q_(m) can, in fact, be produced in series by oneand the same quality measure generator. Furthermore, results obtained ina first quality measure generator, e.g. 101, may be re-used in secondquality measure generator, e.g. 102 or 103, in order to reduce thenumber of calculations. Each quality measure generator 101-103 alsoincludes an interference suppressor, which suppresses unwanted signalcomponents in the incoming signal r(n). Hence, the interferencesuppressor performs an interference rejection for the incoming signalr(n) received in one reception branch in consideration of a temporalwhitening, or for separate versions of the incoming signal r(n) receivedvia two or more reception branches, either in consideration of a spatiodecorrelation of the separate versions or in combined consideration oftemporal whitening and spatio decorrelation.

According to one embodiment of the invention, the quality measuresq₁-q_(m) are determined on basis of a power level of the incoming signalr(n) after interference rejection with respect to a particular detectionhypothesis and a noise power level after interference rejection. As anexample, the power levels may be used to express an SNR, which will beexpounded below.

For reception of the incoming signal in a single branch, the followingquality measure may be used:

$q_{X} = \frac{c_{X}}{{\overset{\sim}{\sigma}}_{X}^{2}}$where {tilde over (σ)}_(X) ² denotes the variance of the residual noiseafter whitening, c_(X) denotes the variance of the desired signal andthe subscript X refers to a particular property hypothesis (or qualitymeasure) for the incoming signal r(n). Thus, the blind detectioninvolves whitening both with respect to the desired signal and theresidual noise.

According to an alternative embodiment of the invention, the residualnoise after whitening instead forms a basis for the blind detection bymeans of the following quality measure:q _(X)=−{tilde over (σ)}_(X) ²where a minimum value of {tilde over (σ)}_(X) ² corresponds to a maximumvalue of the SNR.

The variance of the residual noise {tilde over (σ)}² is obtainedimmediately from the LDA, provided that an AR(2)-model is used. Thus,neither all the AR coefficients need to be calculated nor is itnecessary to explicitly filter the residual noise. In fact, it issufficient to merely calculate the quality measure in consideration of atemporal whitening of the incoming signal as follows:{tilde over (σ)}² =cov(0)a=−cov(1)/{tilde over (σ)}²{tilde over (σ)}²(1−|a| ²){tilde over (σ)}²a=−cov(2)+a·cov(1)/{tilde over (σ)}²{tilde over (σ)}²=(1−|a| ²){tilde over (σ)}²

The covariance function cov(m) of the residual noise e(n) can, forinstance, be estimated as:

${{cov}(m)} = {\sum\limits_{n = L}^{N_{tr} - m}{e*\left( {n + m} \right){e(n)}}}$${{{where}\mspace{14mu}{e(n)}} = {{r\left( {n_{0} + n} \right)} - {\sum\limits_{m = 0}^{L}{{\hat{h}(m)}{s\left( {n - m} \right)}}}}},{L \leq n \leq {N_{tr}.}}$

In order to avoid several divisions in the expressions above, andthereby render the calculations more efficient, the variance of theresidual noise {tilde over (σ)}² can instead be expressed as:

${\overset{\sim}{\sigma}}^{2} = {\frac{\left( {\left( {{cov}(0)} \right)^{2} - {{{cov}(1)}}^{2}} \right)^{2} - {{{{{cov}(0)}{{cov}(2)}} - \left( {{cov}(1)} \right)^{2}}}^{2}}{{{cov}(0)}\left( {\left( {{cov}(0)} \right)^{2} - {{{cov}(1)}}^{2}} \right)}.}$

According to one embodiment of the invention, when the incoming signalis received via multiple branches, the noise in the different branchescan be spatio decorrelated by means of r_(wD)(n)=Dr_(w)(n) and{circumflex over (Q)}⁻¹=D^(H)D, as mentioned earlier. This is appliedwhen searching for the quality measure q_(i) that best fulfills theselection criterion. The procedure thus involves selecting asynchronization position for a sequence of symbols in the incomingsignal r(n). In order to avoid a factorization, which is relativelycomplex from a computational point-of-view, the following qualitymeasure is proposed:q _(X)=Trace{{tilde over (C)}_(rrX) {tilde over (Q)} _(X) ⁻¹}where {tilde over (C)}_(rrX) is the covariance matrix of the incomingsignal r(n) after temporal whitening and Q_(X) is a covariance matrix ofthe residual noise after temporal whitening. The subscript X designatesthe applicable property hypothesis (or quality measure).

According to an alternative embodiment of the invention, where onlyspatio decorrelation is performed, the quality measure may be simplifiedto:q _(X)=Trace{C_(rr) Q _(X) ⁻¹}where C_(rr) is the covariance matrix of the incoming signal r(n). Thecovariance matrix Q_(X) is calculated according toQ _(X) =C _(rr) −C _(rs) C _(ss) ⁻¹ C _(rs) ^(H)as described earlier.

After the spatio decorrelation, the SNR may be expressed as:

${SNR} = \frac{{Trace}\left\{ C_{D} \right\}}{{Trace}\left\{ Q_{D} \right\}}$where C_(D) is the covariance matrix of the desired signal after spatiodecorrelation and Q_(D) denotes the covariance matrix of the noise afterdecorrelation. Hence, the SNR can be written:

${SNR} = {\frac{{Trace}\left\{ {DCD}^{H} \right\}}{{Trace}\left\{ {DQD}^{H} \right\}} = {\frac{{Trace}\left\{ {{CD}^{H}D} \right\}}{{Trace}\left\{ {{QD}^{H}D} \right\}} = \frac{{Trace}\left\{ {\left( {C_{rr} - Q} \right)Q^{- 1}} \right\}}{{Trace}\left\{ {QQ}^{- 1} \right\}}}}$${SNR} = {{\frac{1}{M}{Trace}\left\{ {C_{rr}Q^{- 1}} \right\}} - 1}$

In analogy with q_(X)=−{tilde over (σ)}_(X) ² above, the quality measureq_(X)=−|Q_(X)| can be used as an alternative.

According to one embodiment of the invention, where an incoming signalis received via multiple branches, an SNR-based quality measure

$q_{X} = \frac{c_{X}}{{\overset{\sim}{\sigma}}_{X}^{2}}$can be calculated for each different branch individually. These measuresq_(X) may then be added to form a combined quality measure, whichexclusively takes temporal whitening into account.

In any case, a quality measure comparator 110 receives the qualitymeasures q₁-q_(m) and compares them with each other in order todetermine which quality measure q_(i) that best fulfills a relevantblind selection criterion. Based on the result of this investigation thequality measure comparator 110 identifies a particular property p_(i),which corresponds to the best quality measure q_(i). According to oneembodiment of the invention, the quality measure comparator 110 alsoselects a particular processing unit 121, 122, . . . , 123 for furtherpossible processing of the incoming signal r(n).

If further processing is to take place, the quality measure comparator110 selects the processing unit 122, which corresponds to a qualitymeasure q_(i) that best fulfills the blind selection criterion via acontrol signal c and a multiple switch 125. This processing unit 122 isnamely expected to be capable of handling the incoming signal r(n)optimally by means of applying an appropriate processing principleC_(di), such that a desired resulting signal D_(i) is obtained. In radioapplications, the processing units 121-123 are typically constituted byequalizers. However, technically, they can be represented by anyalternative unit capable of realizing a processing principle, which isadapted to the particular incoming signal r(n).

Moreover, according to one embodiment of the invention, when decodingthe incoming signal r(n), the selected processing unit 122 also re-usesdata having been obtained as a result from the processing in thecorresponding quality measure generator.

FIG. 2 shows a block diagram over a recursive unit 200, which isincluded in the quality measure comparator 110 according to oneembodiment of the invention. A processor 202 in the recursive unit 200receives a preliminary quality measure q_(i)(t) for a current segment ofthe received signal r(n), where t denotes a time index corresponding tothe number of segments previously received. The processor 202 thengenerates an enhanced quality measure q_(i) ^(e)(t) for the segment onbasis of the preliminary quality measure q_(i)(t) and a quality measureq_(i) ^(e)(t−1) for at least one previous segment of the incoming signalr(n), which is stored in a buffer 201. If the current segment is thefirst segment of the received signal r(n), the enhanced quality measureq_(i) ^(e)(t) will be identical with the preliminary quality measureq_(i)(1). The thus generated enhanced quality measure q_(i) ^(e)(t) isthen used as a basis for a current selection decision. The enhancedquality measure q_(i) ^(e)(t) is also fed to the buffer 201 for storage.An enhanced quality measure q_(i) ^(e)(2) for a following segment of thereceived signal r(n) is generated by the processor 202 on basis of acombination of the previous (enhanced) quality measure q_(i) ^(e)(1) anda preliminary quality measure q_(i)(2) for this segment r(n), and so on.

According to a preferred alternative under this embodiment of theinvention, the enhanced quality measure q_(i) ^(e)(t) represents anarithmetic average value between the at least one stored quality measureq_(i) ^(e)(t−1) and the preliminary quality measure q_(i)(t). Thisaveraging increases the reliability of the blind detection and ispreferably carried out with respect to all bursts in a particular radioblock. Optionally, the buffer 201 may be cleared between the separateradio blocks by means of a reset signal z.

For example, the recursive unit 200 may be utilized to improve thequality of selection decisions in GSM/EDGE between the modulationschemes GMSK and 8PSK. In GSM/EDGE a radio block typically consists offour individual bursts. The modulation (GMSK or 8PSK) is one and thesame for all bursts of such a block. In order to take into account thequality measures of all bursts in a particular block when identifyingthe relevant modulation scheme, the arithmetic average of the respectivequality measures for the bursts in the block can, for instance, togetherform a total quality measure. Unfortunately, in a real-time application,this is not always feasible. It is namely common that a further signaltreatment (equalisation, decoding etc) of a first received burst of ablock must commence before the later bursts have been received. Thedecision for the first burst must then rely solely on the qualitymeasure of this burst. However, for the second burst, the qualitymeasures of both the first and the second burst can be used. For thethird burst, all of the preceding bursts may again be used, and so on.In other words, the buffer 201 must at least store quality measures frompreceding bursts belonging to the same block. These quality measures mayeither be stored separately or as one or more accumulated variables.

It should also be noted that, in principle, any computational algorithmwith the quality measures of the current and the preceding bursts may beused to reach the decision as to which modulation scheme is beingapplied. The arithmetic average value thus merely constitutes anexample.

According to another preferred alternative under this embodiment of theinvention, a blind detection decision from any intermediate burst isutilized for equalizing the remaining bursts, for instance, in aparticular radio block. This procedure is advantageous when no or onlyvery few errors can be tolerated in the detected sequence.

In order to sum up, a general method of performing blind detectionaccording to the invention will now be described with reference to aflow diagram in FIG. 3.

A first step 301 receives a segment of an incoming signal, which ispresumed to have a property that is unknown with respect to one out of afinite set of alternatives. Subsequently, a step 302 derives a qualitymeasure for the incoming signal in respect of each of the alternatives.The calculation of the quality measures is carried out in considerationof an interference rejection with respect to any unwanted signalcomponents in the incoming signal. A following step 303 identifies aproperty that represents a best quality measure with respect to aselection criterion. After that, the procedure loops back to the step301 again.

The identification in the step 303 may, for instance, involve a sub-stepin which the quality measures are ranked from a best score down to aworst score. A following sub-step then selects the property beingassociated with the topmost quality measure.

Naturally, all of the process steps, as well as any sub-sequence ofsteps, described with reference to the FIG. 3 above may be carried outby means of a computer program being directly loadable into the internalmemory of a computer, which includes appropriate software for performingthe necessary steps when the program is run on a computer. The computerprogram can likewise be recorded onto arbitrary kind of computerreadable medium.

The term “comprises/comprising” when used in this specification is takento specify the presence of stated features, integers, steps orcomponents. However, the term does not preclude the presence or additionof one or more additional features, integers, steps or components orgroups thereof.

The invention is not restricted to the described embodiments in thefigures, but may be varied freely within the scope of the claims.

1. A method of identifying, from a finite set of alternatives ({p₁, p₂,. . . , p_(m)}), a property of a particular incoming signal (r(n)),comprising the steps of: deriving from the incoming signal (r(n)), foreach of the alternatives, a respective quality measure (q₁, q₂, . . . ,q_(m)) representing a particular property of the incoming signal (r(n));identifying a property (p_(i)) of the incoming signal (r(n)), whichcorresponds to a quality measure (q_(i)) that best fulfills a blindselection criterion; and, deriving the quality measures (q₁, q₂, . . . ,q_(m)) in consideration of interference rejection with respect tounwanted signal components in the incoming signal (r(n)); wherein m is apositive integer and i ranges from 1 to m.
 2. The method according toclaim 1, further comprising the steps of: receiving the incoming signal(r(n)) in one reception branch; and, calculating the quality measures(q₁, q₂, . . . , q_(m)) in consideration of a temporal whitening of theincoming signal (r(n)).
 3. The method according to claim 2, furthercomprising the step of calculating the quality measures (q₁, q₂, . . . ,q_(m)) on the basis of a fictitious temporally whitened version of theincoming signal (r(n)).
 4. The method according to claim 2, wherein thequality measure (q₁, q₂, . . . , q_(m)) is inversely proportional to thevariance of a residual noise component in a signal resulting from thetemporal whitening.
 5. The method according to claim 1, furthercomprising the steps of: receiving separate versions of the incomingsignal (r(n)) via at least two different reception branches; and,calculating the quality measures (q₁, q₂, . . . , q_(m)) inconsideration of a spatio decorrelation of the separate versions of theincoming signal (r(n)).
 6. The method according to claim 5, furthercomprising the step of calculating the quality measures (q₁, q₂, . . . ,q_(m)) in a combined consideration of the temporal whitening of theincoming signal (r(n)) and the spatio decorrelation of the separateversions of the incoming signal (r(n)) received via the at least twodifferent reception branches.
 7. The method according to claim 1,further comprising the step of calculating the quality measures (q₁, q₂,. . . , q_(m)) on the basis of a fictitious spatially decorrelatedversion of the incoming signal (r(n)).
 8. The method according to claim7, wherein the step of calculating the quality measures (q₁, q₂, . . . ,q_(m)) comprises the step of selecting a synchronization position for asequence of symbols in the incoming signal (r(n)) by means of tracesynchronization.
 9. The method according to claim 7, wherein the step ofcalculating the quality measures (q₁, q₂, . . . , q_(m)) comprises thestep of selecting a synchronization position for a sequence of symbolsin the incoming signal (r(n)) by means of determinant synchronization.10. The method according to claim 1, further comprising the steps of:storing at least one quality measure (q_(i) ^(e)(t−1)) for at least oneprevious segment of the incoming signal (r(n)); and, generating anenhanced quality measure (q^(e)(t)) for a current segment of thereceived signal (r(n)) on the basis of the at least one stored qualitymeasure (q_(i) ^(e)(t−1)) and a preliminary quality measure (q_(i)(t))for the current segment of the incoming signal (r(n)).
 11. The methodaccording to claim 10, wherein the enhanced quality measure (q_(i)^(e)(t)) represents an arithmetic average value between the at least onestored quality measure (q_(i) ^(e)(t−1)) and the preliminary qualitymeasure (q_(i)(t)).
 12. The method according to claim 1, wherein thefinite set of alternatives ({p₁, p₂, . . . , p_(m)}) includes at leasttwo different demodulation schemes.
 13. The method according to claim12, wherein the finite set of alternatives ({p₁, p₂, . . . , p_(m)})includes at least one of a Gaussian Minimum Shift Keying scheme and anM-ary Phase Shift Keying scheme.
 14. The method according to claim 1,wherein the finite set of alternatives ({p₁, p₂, . . . , p_(m)})includes at least two different symbol sequences.
 15. The methodaccording to claim 14, wherein each of the symbol sequences represents aparticular training sequence.
 16. The method according to claim 1,wherein each quality measure (q₁, q₂, . . . , q_(m)) is determined onthe basis of a power level of the incoming signal (r(n)) afterinterference rejection and a noise power level after interferencerejection.
 17. The method according to claim 1, wherein each qualitymeasure ({q₁, q₂, . . . , q_(m)}) is represented by a signal-to-noiseratio.
 18. The method according to claim 1, wherein the incoming signal(r(n)) is a radio signal.
 19. A computer readable medium storing codes(or instruction) in such a manner as to be readable and executable by acomputer for performing the steps of claim
 1. 20. A computer readablemedium having a program recorded therein in such as manner to bereadable and executable by a computer for performing the steps ofclaim
 1. 21. The blind signal detector for receiving an incoming signal(r(n)) and automatically identifying a property of a particular incomingsignal (r(n)) from a finite set of alternatives ({p₁, p₂, . . . ,p_(m)}), comprising: at least two quality measure generators (101-103),each receiving the incoming signal (r(n)), and in response thereto,producing a respective quality measure (q₁, q₂, . . . , q_(m))representing a particular property in the finite set of alternatives({p₁, p₂, . . . ,P_(m)}); a quality measure comparator (110) forreceiving the quality measures ({q₁, q₂, . . . , q_(m)}), comparing thequality measures (q₁, q₂, . . . , q_(m)) with each other, andidentifying a property (p_(i)) of the incoming signal (r(n)) whichcorresponds to the quality measure (q_(i)) that best fulfills a blindselection criterion; and, wherein each quality measure generator(101-103) includes an interference suppressor which suppresses theeffects of unwanted signal components in the incoming signal (r(n));wherein m is a positive integer and i ranges from 1 to m.
 22. The blindsignal detector according to claim 21, wherein: the incoming signal(r(n)) is fed to the at least two quality measure generators (101-103)via one reception branch; and, the at east two quality measuregenerators (101-103) are adapted to calculate the quality measures (q₁,q₂, . . . , q_(m)) in consideration of a temporal whitening of theincoming signal (r(n)).
 23. The blind signal detector according to claim21, wherein: the incoming signal (r(n)) is fed to the at least twoquality measure generators (101-103) via at least two differentreception branches; and, the at least two quality measure generators(101-103) are adapted to calculate the quality measures (q₁, q₂, . . . ,q_(m)) in consideration of a spatio decorrelation of the separateversions of the incoming signal (r(n)).
 24. The blind signal detectoraccording to claim 23, wherein the at least two quality measuregenerators (101-103) are adapted to calculate the quality measures (q₁,q₂, . . . , q_(m)) in a combined consideration of the temporal whiteningof the incoming signal (r(n)) and the spatio decorrelation of theseparate versions of the incoming signal (r(n)) received via the atleast two different reception branches.
 25. The blind signal detectoraccording to claim 21, wherein: at least two of the at least two qualitymeasure generators (101-103) are co-located in a single unit; and, theoperation of the respective quality measure generator (101-103) isdetermined by a value of at least one variable parameter.
 26. The blindsignal detector according to claim 21, wherein at least one firstquality measure generator (101) delivers a calculation result to atleast one second quality measure generator (102; 103).
 27. The blindsignal detector according to claim 21, wherein the quality measurecomparator (110) includes a recursive unit (200) for storing at leastone quality measure (q_(i) ^(e)(t−1)) for at least one previous segmentof the incoming signal (r(n)) and generating an enhanced quality measure(q_(i) ^(e)(t)) for a current segment of the received signal (r(n)) onbasis of the at least one stored quality measure (q_(i) ^(e)(t−1)) and apreliminary quality measure (q_(i)(t)) for the current segment of thereceived signal (r(n)).